Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’. I also used the ‘scales’ package which I loaded here as well.
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## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1
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## Vedhæfter pakke: 'scales'
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## col_factor
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
A logarithmic axis linearises exponential growth. It more clearly shows the development.
The answer is Kuwait. The country had a GDP per capita of 108382.3529 in 1952. I got the answer by isolating the 1952 information about country and GDP per capita into a new dataframe, gdpPercap_1952 and then ordering this in a decreasing order by GDP pr. capita.
gdpPercap_1952 <- gapminder %>%
filter(year == 1952) %>%
select(country, gdpPercap)
gdpPercap_1952[order(gdpPercap_1952$gdpPercap,decreasing=TRUE),]
## # A tibble: 142 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Kuwait 108382.
## 2 Switzerland 14734.
## 3 United States 13990.
## 4 Canada 11367.
## 5 New Zealand 10557.
## 6 Norway 10095.
## 7 Australia 10040.
## 8 United Kingdom 9980.
## 9 Bahrain 9867.
## 10 Denmark 9692.
## # … with 132 more rows
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
I added another argument in the aesthetic mapping for color and tied it to continent. Then I used the scale function to change the axis labels and units to no longer be scientific notations.
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() +
scale_x_log10(labels = label_number())+
scale_size_continuous(labels = label_number())
The world’s five richest countries in 2007 were Norway, Kuwait, Singapore, United States, and Ireland. I did the same thing as with 1952 and just counted the five first countries (there is probably a better way to do this, though).
gdpPercap_2007 <- gapminder %>%
filter(year == 2007) %>%
select(country, gdpPercap)
gdpPercap_2007[order(gdpPercap_2007$gdpPercap,decreasing=TRUE),]
## # A tibble: 142 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.
## 6 Hong Kong, China 39725.
## 7 Switzerland 37506.
## 8 Netherlands 36798.
## 9 Canada 36319.
## 10 Iceland 36181.
## # … with 132 more rows
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
transition_states() and transition_time() functions respectively)See below.
I combined question 5 and 6:
anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
labs(title = "Year: {as.integer(frame_time)}",
x = "GDP per capita",
y = "Life expectancy (years)",
size = "Population",
colour = "Continent") +
scale_x_log10(labels = label_number())+
scale_size_continuous(labels = label_number())+
transition_time(year)
anim3
gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]I attempted to answer the following question: What was the mean life expectancy across continents the year I turned 1 (1997) versus the year my father turned 31 (1967)?
I tried out a few different ways of answering and illustrating this. Firstly, I created a new dataframe mean_lifeExp6797 in which I only included information about continent, year and life expectancy. I then grouped by continent and year and summarised the life expectancy column into a new column showing the mean values per year per continent. I then filtered the dataframe to only show the years relevant to answering my question, namely 1967 and 1997.
mean_lifeExp6797 <- gapminder %>%
select(continent,year, lifeExp) %>%
group_by(continent,year) %>%
summarise(mean_life_exp = mean(lifeExp)) %>%
filter(year==1967|year==1997)
## `summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.
mean_lifeExp6797
## # A tibble: 10 × 3
## # Groups: continent [5]
## continent year mean_life_exp
## <fct> <int> <dbl>
## 1 Africa 1967 45.3
## 2 Africa 1997 53.6
## 3 Americas 1967 60.4
## 4 Americas 1997 71.2
## 5 Asia 1967 54.7
## 6 Asia 1997 68.0
## 7 Europe 1967 69.7
## 8 Europe 1997 75.5
## 9 Oceania 1967 71.3
## 10 Oceania 1997 78.2
Then I tried plotting the information in two different ways. The most tricky part of this was getting the plots to show years as separate values and not as a scale. I fixed this by making a new dataframe, mean_lifeExp_6797_plotting with a column where the values in the ‘year’ column had been converted into characters.
I then made a barplot with this new dataframe comparing the mean life expectancy of all continents in 1967 versus 1997:
mean_lifeExp6797_plotting <- mean_lifeExp6797 %>%
mutate(year_character = as.character(year))
ggplot(mean_lifeExp6797_plotting, aes(x = year_character, y = mean_life_exp, fill = continent)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Mean life expectancy per continent in 1967 versus 1997",
x = "Year",
y = "Mean life expectancy (years)",
fill = "Continent")
Finally, I used faceing to create a collection of small barplots, one per continent, comparing mean life expectancy in 1967 versus 1997. By making both types of barplot, you can compare the development of mean life expectancy both within the resepective continents as well as between continents of the world.
ggplot(mean_lifeExp6797_plotting, aes(x = year_character, y = mean_life_exp, fill = year_character)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title="Mean life expectancy per continent in 1967 versus 1997",
x = "Continent",
y = "Mean life expectancy (years)",
fill = "Year") +
facet_wrap(~ continent)